By the end of 2025, Gartner predicts that 30% of generative AI (GenAI) projects will be abandoned after the proof of concept (PoC) stage. This means that after companies try out GenAI and see what it can do, nearly a third of them might decide not to move forward into production. And honestly, that number might even be on the low side.

Even though I'm a big fan of GenAI, I understand how tough it can be to move from a successful demo to full-scale production implementation.

Let’s talk about why this happens.

The Common Challenges

1. Poor Data Quality: GenAI is only as good as the data it’s trained on. If the data is messy, incomplete, or just not relevant, the AI can’t perform well. Imagine trying to bake a cake with spoiled ingredients – it’s just not going to turn out right.
2. Inadequate Risk Controls: As powerful as GenAI is, it can also introduce risks, like making decisions that might be biased or unpredictable. Without strong risk management in place, companies might not feel safe enough to rely on AI.
3. Escalating Costs: Implementing AI isn’t cheap. While a small project might be affordable, scaling it up to a full production system can get expensive fast. Costs for computing power, storage, and expertise can quickly spiral out of control.
4. Unclear Business Value: It’s easy to get excited about what AI could do, but if it’s not clear how it actually helps the business, the project can lose sponsorship. If the benefits aren’t obvious, decision-makers might pull the plug.

‍

Rushing Ahead Without a Plan

Many companies are eager to jump on the GenAI bandwagon, but there’s still a lot that needs to be figured out. Before they can really make the most of AI, they need to go through what’s called a “Data maturity cycle.” This means improving how they handle and understand their data, particularly in terms of quality and Governance.

‍

The Maturity Process

Here’s a simplified version of the maturity process:

1. Start with Data: Gather relevant data and set up processes to generate the data you’ll need.
2. Basic AI Models: Begin with simple models that describe what’s happening in your business. Train, validate, deploy, and maintain these models.
3. Knowledge and Experimentation: Build knowledge graphs (which show how different pieces of information are related) and start experimenting with more complex business questions.
4. Advanced AI: Move on to more complex machine learning and deep learning models that can make predictions or automate decision-making.
5. AI-Driven Systems: Finally, transition from digital twins (virtual models of processes or products) to intelligent twins that can learn and adapt over time.

Each of these steps builds on the last, making it easier and cheaper to develop advanced AI capabilities.

‍

In Summary

Moving from a GenAI proof of concept to a full-scale project isn’t easy, but it’s doable with the right preparation and partnership. Companies need a clear data strategy and must understand that it’s a step-by-step process. It takes time, investment, and a willingness to learn from each stage. But for those who get it right, the rewards can be significant.

‍

Ready to accelerate your business growth, drive collaboration and up level every decision in your organization through next generation AgentOS? Learn more by exploring the types of Agents you can accomplish through DataGOL, and sign up for DataGOL Platform Trial the Box to see it working. If you are looking for an enterprise ready AI platform and would like to learn more about DataGOL, signup for a personalised demo today!